Conventional processing units have become increasingly complex to enhance the computational sophistication of the individual processing units. Recently, attempts have been made to solve complex computational problems using (simulated) neural networks. However, such attempts typically have relied on the infrastructure of conventional processing units designed to perform conventional computer processing. As such, conventional neural computing may be restricted in efficiency, effectiveness, and/or cost by the mismatch between computing resources and computing philosophy.